{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variable transformers : PowerTransformer\n",
    "\n",
    "The PowerTransformer() applies power or exponential transformations to\n",
    "numerical variables.\n",
    "\n",
    "The PowerTransformer() works only with numerical variables.\n",
    "\n",
    "**For this demonstration, we use the Ames House Prices dataset produced by Professor Dean De Cock:**\n",
    "\n",
    "Dean De Cock (2011) Ames, Iowa: Alternative to the Boston Housing\n",
    "Data as an End of Semester Regression Project, Journal of Statistics Education, Vol.19, No. 3\n",
    "\n",
    "http://jse.amstat.org/v19n3/decock.pdf\n",
    "\n",
    "https://www.tandfonline.com/doi/abs/10.1080/10691898.2011.11889627\n",
    "\n",
    "The version of the dataset used in this notebook can be obtained from [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from feature_engine.imputation import ArbitraryNumberImputer\n",
    "from feature_engine.transformation import PowerTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "3   2006        WD        Abnorml     140000  \n",
       "4   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "\n",
    "data = pd.read_csv('houseprice.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1022, 79), (438, 79))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's separate into training and testing set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(['Id', 'SalePrice'], axis=1), data['SalePrice'], test_size=0.3, random_state=0)\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PowerTransformer(variables=['LotArea', 'GrLivArea'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize Transformers with exponent 1/2\n",
    "# this is equivalent to square root\n",
    "# we will transform only 2 variables\n",
    "\n",
    "et_transformer = PowerTransformer(variables=['LotArea', 'GrLivArea'], exp=0.5)\n",
    "\n",
    "et_transformer.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform variables\n",
    "\n",
    "train_t = et_transformer.transform(X_train)\n",
    "test_t = et_transformer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'GrLivArea')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# variable before transformation\n",
    "X_train['GrLivArea'].hist(bins=50)\n",
    "plt.title('Variable before transformation')\n",
    "plt.xlabel('GrLivArea')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'GrLivArea')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# transformed variable\n",
    "train_t['GrLivArea'].hist(bins=50)\n",
    "plt.title('Transformed variable')\n",
    "plt.xlabel('GrLivArea')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'LotArea')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# tvariable before transformation\n",
    "X_train['LotArea'].hist(bins=50)\n",
    "plt.title('Variable before transformation')\n",
    "plt.xlabel('LotArea')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'LotArea')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# transformed variable\n",
    "train_t['LotArea'].hist(bins=50)\n",
    "plt.title('Variable before transformation')\n",
    "plt.xlabel('LotArea')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# return variables to original representation\n",
    "\n",
    "train_orig = et_transformer.inverse_transform(train_t)\n",
    "test_orig = et_transformer.inverse_transform(test_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inverse transformed variable distribution\n",
    "\n",
    "train_orig['LotArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inverse transformed variable distribution\n",
    "\n",
    "train_orig['GrLivArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automatically select numerical variables\n",
    "\n",
    "To use the PowerTransformer we need to ensure that numerical values don't have missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove missing data \n",
    "\n",
    "arbitrary_imputer = ArbitraryNumberImputer()\n",
    "\n",
    "arbitrary_imputer.fit(X_train)\n",
    "\n",
    "# impute variables\n",
    "train_t = arbitrary_imputer.transform(X_train)\n",
    "test_t = arbitrary_imputer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PowerTransformer(exp=2)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initialize transformer with exp as 2\n",
    "\n",
    "et = PowerTransformer(exp=2, variables=None)\n",
    "\n",
    "et.fit(train_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MSSubClass',\n",
       " 'LotFrontage',\n",
       " 'LotArea',\n",
       " 'OverallQual',\n",
       " 'OverallCond',\n",
       " 'YearBuilt',\n",
       " 'YearRemodAdd',\n",
       " 'MasVnrArea',\n",
       " 'BsmtFinSF1',\n",
       " 'BsmtFinSF2',\n",
       " 'BsmtUnfSF',\n",
       " 'TotalBsmtSF',\n",
       " '1stFlrSF',\n",
       " '2ndFlrSF',\n",
       " 'LowQualFinSF',\n",
       " 'GrLivArea',\n",
       " 'BsmtFullBath',\n",
       " 'BsmtHalfBath',\n",
       " 'FullBath',\n",
       " 'HalfBath',\n",
       " 'BedroomAbvGr',\n",
       " 'KitchenAbvGr',\n",
       " 'TotRmsAbvGrd',\n",
       " 'Fireplaces',\n",
       " 'GarageYrBlt',\n",
       " 'GarageCars',\n",
       " 'GarageArea',\n",
       " 'WoodDeckSF',\n",
       " 'OpenPorchSF',\n",
       " 'EnclosedPorch',\n",
       " '3SsnPorch',\n",
       " 'ScreenPorch',\n",
       " 'PoolArea',\n",
       " 'MiscVal',\n",
       " 'MoSold',\n",
       " 'YrSold']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# variables to trasnform\n",
    "et.variables_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AD7VPLvcB17ajMTYD5wHfXpGdGIDM/ExmbsrMCTpfSj6Umdcx4nVZkmF/i1rxBnwVOAT8jM582w105uIeBJ4B/gk4o20bdH744llgHzA553V+m84XLgeATwx7v/pQl1+jMz3yXeDxdvvwqNcG+GXgsVaXJ4A/b+3voBM0B4CvAae09lPb+oH2+DvmvNaftXo9DXxo2PvWxxpN8eZRKNalx5un0ktSUU6hSFJRBrgkFWWAS1JRBrgkFWWAS1JRBrgkFWWAS1JR/w+dfRazql6e0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# before transformation\n",
    "\n",
    "train_t['GrLivArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform variables\n",
    "\n",
    "train_t = et.transform(train_t)\n",
    "test_t = et.transform(test_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# transformed variable\n",
    "train_t['GrLivArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# return variables to original representation\n",
    "\n",
    "train_orig = et_transformer.inverse_transform(train_t)\n",
    "test_orig = et_transformer.inverse_transform(test_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fB559lS5rgNuz7UFgSUSc1pvZ9c806lKtzDyYmQ+V7eeBvbSvMjDenH/czPfQn+qyDhP/I7zSJzOPAkeAU3syu/6aTm0Afqf8Gbo1IlZMcbxW061fjd4TEd+JiK9ExG/0ezL9UJaJ3wnsnHBozj9u5nvoq5l/BQYz8zeB+/j5X0RSJw8Bb87MtwO3AP/S3+n0XkQsBr4MfDIzf9Lv+czUfA/96VzW4ZU+EbEIOBl4piez669j1iYzn8nM/ym7XwDe1aO5zQdeMmQKmfmTzBwr2/cCx0fE0j5Pq2ci4njagb85M++eosucf9zM99CfzmUdtgNry/bFwP1Z3nFZ4I5ZmwlrjRfSXqNU23bg8vJpjFXAkcw82O9J9VtE/PLL74lFxNm0M6SGF1GU+70R2JuZn+3Qbc4/bubcVTZnIjtc1iEiPg3syszttP8j/XNEjNJ+g+rS/s24d6ZZm09ExIXAUdq1uaJvE+6xiLiD9idRlkbEfuB64HiAzPw8cC/tT2KMAi8CV/Znpr01jbpcDPxBRBwFXgIureRFFMA5wMeAPRHxcGn7FPArMH8eN16GQZIqMt+XdyRJM2DoS1JFDH1JqoihL0kVMfQlqceOdWG7CX2vKRd5eyQidkTEm8cd+2pEPBcR90z33Ia+JPXeF4Hzptn328BQ+eb8VuCvxx37G9ofI502Q1+SemyqC9tFxK+VV+67I+IbEfHW0veBzHyxdHuQ9rd8Xx5nB/D8TM49r7+cJUkLyAjw+5n5/Yh4N/CPwPsn9FlH+9LNXTP0JanPykXcfgv40rgrv584oc/vAkPA+5qcy9CXpP57HfBcZr5jqoMR8QHgz4D3jbtIYtcnkiT1UblE8w8i4iPwyj+7+Pay/U7gn4ALM/NQ03N57R1J6rHxF7YDnqZ9Ybv7gVtp/7OMxwNbMvPTEfEfwJnAy1frfDIzLyzjfAN4K7CY9tVO12Xm11713Ia+JNXD5R1JqoihL0kVMfQlqSKGviRVxNCXpIoY+pJUEUNfkiry/woO9PypEQQ0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inverse transformed variable distribution\n",
    "\n",
    "train_orig['LotArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inverse transformed variable distribution\n",
    "\n",
    "train_orig['GrLivArea'].hist(bins=50)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fenotebook",
   "language": "python",
   "name": "fenotebook"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
